The results reported in Table REF  indicate that TAGI matches the performance of existing methods in term of root mean square error (RMSE) and log-likelihood (LL). Even if VMG displays the best predictive performance, its computational time is two order of magnitude greater than TAGI, PBP and MC-Dropout [1]}. The current TAGI's implementation has a computation time that is comparable to PBP and MC-Dropout [2]}. The timing details for each dataset is reported in the Appendix . Because TAGI had to be implemented from scratch, it is currently not yet fully optimized for computational efficiency. The same is true for the optimization of hyper-parameters such as \(\sigma _{V}\)  which currently rely on a gradient-based approach. Furthermore, the results reported for TAGI did not employ dropout [3]}. Also, as it is the case for the other methods reported in Table REF , the number of epochs employed was not optimized.
